In the area of integrated navigation, it is common for a system to combine data from inertial navigation sensors with data received from Global Positioning System (GPS) satellites, or more generally, Global Navigation Satellite Systems (GNSS). A common method used for combining these two sources of data is the use of the Kalman filter algorithm. The Kalman filter algorithm has been in use in integrated navigation systems for over 20 years. The filter blends GPS signal data with inertial navigation data by primarily depending on GPS signals for long term accuracy and inertial navigation data for short time frames. Hence, the navigation system has the low noise and short-term accuracy of the inertial sensors, while not suffering from integration drift common to inertial navigation data because the navigation system depends on GPS signals for long time frame calculations.
In combining the two sources of data, it is desirable to know the quality of the GPS measurements. If the GPS signals are suffering from high levels of interference, such as from jamming, the GPS measurements should in most circumstances be weighted less in the combination with the low noise inertial sensor data. If, however, the GPS signal is suffering from little noise and thus very accurate, the GPS measurements should in most circumstances be weighted more in the combination of the two data sources. It is common, therefore, for a GPS receiver to include a low pass time constant filtered estimator which calculates a signal power to noise power ratio (SNR), or alternatively a carrier power to noise power spectral density (C/No) ratio, for each GPS satellite being tracked. However, typical time constant filtered estimators respond slowly to changes in the SNR, especially during high interference when the SNR is low. Under low SNR conditions, the SNR estimates must be heavily filtered to provide acceptable accuracy. The required long filtering time constants make it difficult to get fast-responding accurate estimates of SNR during high interference using a typical time constant filtered estimator.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for a signal to noise estimator which responds quickly to changes in noise level and provides accurate estimates of signal to noise ratio during periods of high interference.